code: def main(cfg) -> None:
      trainer = Trainer(strategy=NLPDDPStrategy(), **cfg.trainer)
  
      model_path = cfg.retro_model_file
  
      save_restore_connector = NLPSaveRestoreConnector()
  
      if os.path.isdir(model_path):
          save_restore_connector.model_extracted_dir = model_path
  
      model_cfg = MegatronRetrievalModel.restore_from(
          model_path,
          trainer=trainer,
          return_config=True,
          save_restore_connector=save_restore_connector,
      )
  
      with open_dict(model_cfg):
          model_cfg.precision = trainer.precision
          model_cfg.sequence_parallel = False
          model_cfg.activations_checkpoint_granularity = None
          model_cfg.activations_checkpoint_method = None
  
      model = MegatronRetrievalModel.restore_from(
          model_path,
          trainer=trainer,
          save_restore_connector=save_restore_connector,
          override_config_path=model_cfg,
      )
  
      # check whether the DDP is initialized
      if parallel_state.is_unitialized():
  
          def dummy():
              return
  
          if model.trainer.strategy.launcher is not None:
              model.trainer.strategy.launcher.launch(
                  dummy, trainer=model.trainer
              )
          model.trainer.strategy.setup_environment()
  
      retrieval_service = OmegaConf.to_container(cfg.retrieval_service)
      model.set_inference_config(None, retrieval_service)
  
      # running text generation, use inference server
      if (
          parallel_state.is_pipeline_first_stage()
          and parallel_state.get_tensor_model_parallel_rank() == 0
      ):
          server = MegatronServer(
              model.cuda(), inference_strategy=model.inference_strategy
          )
          server.run("0.0.0.0", port=cfg.port)
  
      while True:
          choice = torch.cuda.LongTensor(1)
          torch.distributed.broadcast(choice, 0)
          if choice[0].item() == 0:
              generate(model.cuda(), strategy=model.inference_strategy)
  
file_path: NeMo/scripts/nlp_language_modeling/service_launch_scripts/start_retro_model_service.py
imports: import os
  import torch
  from omegaconf.omegaconf import OmegaConf, open_dict
  from pytorch_lightning import Trainer
  from nemo.collections.nlp.models.language_modeling.megatron_retrieval_model import MegatronRetrievalModel
  from nemo.collections.nlp.modules.common.text_generation_server import MegatronServer
  from nemo.collections.nlp.modules.common.text_generation_utils import generate
  from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy, NLPSaveRestoreConnector
  from nemo.core.config import hydra_runner
label_desc: Use image node_id 1 for calling a global function with example usage: main(cfg) without return types